Bayesian Modeling for the Detection of Adverse Events Underreporting in Clinical Trials

Author:

Barmaz YvesORCID,Ménard TimothéORCID

Publisher

Springer Science and Business Media LLC

Subject

Pharmacology (medical),Pharmacology,Toxicology

Reference22 articles.

1. Medicine And Healthcare Products Regulatory Agency. Gcp Inspection Metrics Report. 2018. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/706356/GCP_INSPECTIONS_METRICS_2016-2017__final_11-05-18_.pdf. Accessed 18 Dec 2020.

2. International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use. E26(R2) Guideline for Good Clinical Practices. 2016. https://database.ich.org/sites/default/files/E6_R2_Addendum.pdf Accessed 18 Dec 2020.

3. Ménard T, Barmaz Y, Koneswarakantha B, Bowling R, Popko L. Enabling data-driven clinical quality assurance: predicting adverse event reporting in clinical trials using machine learning. Drug Saf. 2019;42(9):1045–53.

4. Ménard T, Koneswarakantha B, Rolo D, Barmaz Y, Bowling R, Popko L. Follow-up on the use of machine learning in clinical quality assurance: can we detect adverse event under-reporting in oncology trials? Drug Saf. 2020;43(3):295–6.

5. Food and Drug Administration. Clinical Investigator Inspection List (CLIIL). https://www.fda.gov/drugs/drug-approvals-and-databases/clinical-investigator-inspection-list-cliil. Accessed 18 Dec 2020.

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